Furthermore, we applied the PAM and \(M_F\)  attention modules to the StleGAN2 [1]} architecture. We also added the proposed Banach space norms (SSIM NIQE) to compare the overall model performance with our model. In Table 5, we show the trade-offs of the QAGAN [2]}, StyleGAN2 [1]} and our model baseline (BL). We used different combinations of the standard IQA metrics as discussed in section 4.1 and 4.2. Our findings confirm that our approach is competitive with state-of-the-art. Most importantly, we see improved performance of our model at lower resolutions (32 x 32), this improvement can be attributed to the attention schema employed. In Figure 9, we showcase the performance of QAGAN [2]} and our model on image synthesis for CelebA dataset. Our results show that our model performs significantly well overall, Table 5 gives a clearer representation of the performance levels.
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